Steffen Lohrey

Expectations for new temperature records derived from climate projections have recently been outpaced by record-shattering events. Such events pose a serious hazard to societies Not all physical mechanisms are well-understood. Open questions also remain on the coordinated and uncoordinated human responses. Insights into societal reactions to such outlier records are important for designing adaptation strategies, and for anticipating societal dynamics.We hypothesize that extreme heat events may trigger societal response. Therefore, we design a statistical framework to explore heat record exceedance in recent decades and combine it with socioeconomic impact and response data to elucidate event-response relationships. More specifically, we assess air conditioning uptake in Europe and heat-health impacts. As meteorological baseline we use daily maximum temperature and compare it with annual air-conditioning data at country-level, global burden of disease reports, and socio-economic variables. We validate our hypothesis using both fixed effects regression models and event coincidence analysis. We first find that while temperature records show a strong upward trend in entire Europe, the occurrence of large temperature record exceedance is spatially heterogeneous. Fixed effects analyses show a statistically significant effect of highest temperature and gross-domestic product on air-conditioning uptake. They also highlight the importance of a one-year time lag between highest temperature and the air-conditioning data. Further, event coincidence analysis points at an impact of single heat events on air-conditioning uptake.Overall, our results attempt to shed light onto an issue that is of urgent societal importance in the face of new records. Insights into the driving role of single record-breaking events are very valuable for informing adaptation measures, wider policies, but also early warning systems and approaches related to anticipatory action.

Cities face multiple growing climate-related risks. Identifying adaptation strategies and quantifying their effectiveness and limits is hence crucial. Street green space (SGS) receives significant attention in the urban heat adaptation space due to its potential to reduce heat load and provide additional ecosystem benefits. Yet, the majority of existing studies assessing the effectiveness of SGS are either global or very local, and typically rely on remotely-sensed surface temperature and green space density metrics. Limited evidence spanning across different urban and climate contexts exists. Here, we empirically estimate the heat stress reduction potential of SGS across global and local climate zones in 133 cities worldwide using air temperature and wet-bulb globe temperature (WBGT) daily outputs from UrbClim, a 100-m resolution urban microclimate model, combined with a high-resolution SGS indicator, the Green View Index (GVI). We quantify a SGS cooling efficiency interquartile range of [-0.03, -0.01] °C/GVI for maximum WBGT, with substantial variation across global climate and local climatic zones. We design reality-bounded scenarios to explore possible evolutions of SGS until 2050. Combining these scenarios with the estimated cooling efficiencies, we show that ambitious yet locally feasible SGS expansion could offset 3-11% (cities interquartile range) of the projected increase in maximum WBGT under a current policies climate change scenario, and 2-7% under SSP5-(8.5), compared to a 2008-2017 climatology. These results highlight that SGS expansion is an effective yet insufficient strategy to adapt to the growing urban heat stress across cities worldwide. Conversely, reduced SGS from administrative inaction or climate impacts on vegetation health may worsen urban heat. These findings inform about the global adaptation potential and limits of urban street green, and can support policymakers in framing SGS expansion programs into a broader portfolio of actions to tackle growing urban heat and its adverse consequences.

Vegetation in the street can lower temperatures at neighbourhood level and reduce heat stress for pedestrians. Street green spaces (SGS) is thus an urgently needed nature-based solution for adapting to a warming climate, and also has some ability for carbon uptake. This local solution has global potential, but the cooling potential of street green space depends on local context, urban form and climate zone, among others. Here, we provide estimates of cooling potential of SGS for different climate zones, world regions and city type oriented along a typology recently provided. We find that peak wet bulb globe temperatures are relevantly reduced, and that the effect is greatest for large cities in the tropics and in temperate climates. Cooling from vegetation can only moderate the effect of global warming, not compensate for it. Carbon sequestration potential remains limited at 0.1 Gt CO2/yr, possibly double, if soil carbon is included, with high uncertainties. Street green will continue to be invaluable for local quality of life and be one contributor to reducing temperatures, but will not replace larger climate mitigation efforts.

Climate projections suggest greatly increased exposure to heat, and they have recently been outpaced by record-shattering heat events. Not all physical mechanisms are understood, and many open questions remain on the coordinated and uncoordinated human responses to record-breaking events. Insights into societal reactions to such outlier records are important for designing adaptation strategies, and for anticipating societal dynamics.We hypothesize heat extremes trigger societal response. Therefore, we design a statistical framework to explore heat record exceedance in recent decades and combine it with socioeconomic impact and response data to elucidate event-response relationships. More specifically, we assess air conditioning uptake in Europe and heat-health impacts. As meteorological baseline we use daily maximum temperature and compare it with annual air-conditioning data at country-level, global burden of disease reports, and socio-economic variables. We validate our hypothesis using both fixed effects regression models, and event coincidence analysis. We first find that while temperature records show a strong upward trend in entire Europe, the occurrence of large temperature record exceedance is spatially heterogeneous. Fixed effects analyses show a statistically significant effect of highest temperature and gross-domestic product on air-conditioning uptake. They also highlight the importance of a one-year time lag between highest temperature and the air-conditioning data. Further, event coincidence analysis points at an impact of single heat events on air-conditioning uptake.Overall, our results show promising insights into an issue that is of urgent societal importance in the face of new records. Insights into the driving role of single record-breaking events are very valuable for informing adaptation measures, wider policies, but also early warning systems and approaches related to anticipatory action.

The devastating 2022 summer flood in Pakistan displaced about 7 million people in the Sindh province alone. Up to one‐third of the country's area, mostly the country's south, was flooded. Effective response to intensifying and compounding hazards requires a better understanding of these processes. We can gain insights if impact assessments include socio‐economic components and uncertainties arising from the interactions between impacts. However, the quantitative evidence from impact assessments remains limited and fragmented, due to methodological challenges and data limitations. Using the open‐source impact assessment platform CLIMADA, we study to what extent flood‐related hazards can be used to quantify displacement outcomes in a data‐limited region. Using flood depth, exposed population, and impact functions, we link flood vulnerability to displaced people. This allows us to estimate internal displacement resulting from the flood event, and to further assess how displacement varies across the region. We find that a flood depth threshold of 0.67 m, with a confidence interval (CI) from 0.35 to 1.10 m, provides a best fit to all data from Sindh province. We find a negative correlation between displacement and the degree of urbanization. By testing the performance of our model in explaining differing displacement estimates reported across Pakistan, we show the limitations of existing impact assessment frameworks. We emphasize the importance of estimating potential displacement alongside other impacts to better characterize, communicate, and ultimately mitigate the impacts of flooding hazards.

The devastating 2022 summer flood in Pakistan displaced about 7 million people in the Sindh province alone. Up to one-third of the country’s area, mostly the country’s south, was flooded. Effective response to intensifying and compounding hazards requires a better understanding of these processes. We can gain insights if impact assessments include socio-economic components and uncertainties arising from the interactions between impacts. However, the quantitative evidence from impact assessments remains limited and fragmented, due to methodological challenges and data limitations. Using the open-source impact assessment platform CLIMADA, we study to what extent flood-related hazards can be used to quantify displacement outcomes in a data-limited region. Using flood depth, exposed population, and impact functions, we link flood vulnerability to displaced people. This allows us to estimate internal displacement resulting from the flood event, and to further assess how displacement varies across the region. We find that a flood depth threshold of 0.67 m, with a confidence interval (CI) from 0.35 m to 1.10 m, provides a best fit to all data from Sindh province. We find a negative correlation between displacement and the degree of urbanisation. By testing the performance of our model in explaining differing displacement estimates reported across Pakistan, we show the limitations of existing impact assessment frameworks. We emphasise the importance of estimating potential displacement alongside other impacts to better characterise, communicate, and ultimately mitigate the impacts consequences of flooding hazards.

Urban green has the potential to reduce urban heat stress, with shading and evapotranspiration being both important contributors. We here investigate street green space (SGS), which comprises street-level vegetation in the form of trees, bushes or green facades, i.e. that is “visible to a person in the street”. We measure SGS using the green view index (GVI).We first analyze the relationship between climate zone, urban form and SGS. This is based on published research on current SGS in 181 global cities. We find that observed attainable GVI values vary by urban form, but more importantly, by climate zone at large. Cities in temperate and tropical climates showing much larger values than in dry climate zones. In the next step, we develop three scenarios of future street green space. These are based on the observed greenness from 2016-2023, and on a careful trend analysis. We extend the current values until 2050, using optimistic assumptions that we deem plausible and that have been informed by the trends. In two “optimistic outlook” scenarios, we assume positive trends that are however constrained by maximum observation. This is complemented by a more pessimistic “baseline scenario”, for which we assume current street green space conditions, but negatively impacted by climate change.In a second step we translate these scenarios into a quantifiable perspective of future urban heat mitigation potential for 143 cities using high-resolution climate model data from the URBCLIM project. The cooling potential of SGS by urban form and climate zone has been determined using a random-effects regression analysis with place-specific confounding factors in a related project (EGU presentation EGU25-5169).This work results in a dataset of urban cooling potential of greening scenarios in 143 cities. The scenarios also take into account different climate change scenarios from the RCP-SSP framework.

Urban transformative adaptation is increasingly crucial to minimize the adverse impact of climate change, also in the context of the ongoing global urbanization. Street green space (SGS) represents a key strategy in the solution space due to its capacity to reduce urban heat burden through shade and evapotranspiration. Yet, estimating the cooling efficiency of street trees is highly dependent on the location-specific climate zone, the within-city differences in urban form, as well as on the data and metrics used to measure the urban microclimate and green space density. Moreover, the bulk of previous studies have used remotely sensed land-surface temperature, the use of which is widely criticized for quantifying heat stress.Here we conduct a 100-meter resolution empirical assessment in a globally relevant pool of cities and with a local climate zone (urban form) within-city stratification to re-evaluate the role of street green space in adapting to urban heat in different urban contexts. We measure local heat load using different metrics (wet-bulb globe temperature (WBGT), and average, maximum and minimum 2-meter air temperature), which are calculated from the hourly output of the UrbClim urban climate model for 143 cities across the world, and we use estimates of the Green View Index (GVI) as a street-based measure of tree canopy cover.Using random-effects regression models and controlling for a set of confounding factors in the statistical relation (such as population density, water bodies, and buildings height), we find that street green space is an effective strategy to reduce urban heat, but its effectiveness is highly context- specific, depending on both the local climate and the urban form. Our results can serve to inform the global discourse on transformative change of cities to achieve both adaptation goals (e.g. by reducing health impacts of urban heat or the risk caused by urban hydrological hazards), as well as energy use reduction and emission mitigation targets (e.g. cooling energy needs), also in the light of the upcoming IPCC AR7 special report on cities and climate change.

The 2022 Pakistan floods have been unprecedented in their extent. They affected around 33 million people, caused about 15 billion USD in damages, and took the lives of more than 1,800 persons, dominantly in the southern parts of the country.Effective disaster response requires fast assessments of likely impacts from hazardous weather to inform decision-makers and guide relief efforts for early action. Displacement modeling is a key technique towards these goals. However, displacement modeling which accounts for socio-economic components and uncertainties is methodologically challenging, and quantitative evidence largely remains limited and fragmented. Much work is needed to resolve these.This study aims at providing a case study for disaster displacement modeling by using the open-source impact assessment platform CLIMADA to investigate the extent by which flood-related hazards can be used to quantify displacement numbers in a data-limited region. Here, we estimate displacement from the 2022 Pakistan floods in Sindh province as a case study. We combine data on flood depth, exposed population, and provide impact functions that relate vulnerability of people likely to be displaced. We further use published numbers of affected people as target data for our model. The centerpiece of our analysis is the choice of impact functions. We test different forms of impact functions as well as assumptions about critical flood depths to proxy the number of displaced people, first using ex-ante assumptions, and then a numerically optimized version.With ex-ante assumptions, our model predicts a range of 1.94 to 5.65 million of displaced people in Sindh province, as compared to a total number of 6.76 million as reported by government sources. When we apply numerically optimized impact functions, the results closely resemble those obtained using the ex-ante assumptions, indicating that the current methods underestimate the extent of displacement. Additionally, we have evaluated the relationship between local vulnerability and the level of urbanization, and our findings reveal a negative correlation.We use this model to explain different displacement estimates for the 2022 floods across Pakistan and thereby contribute a case study to the growing field of displacement models, and towards the development of more refined ones. It highlights opportunities as well as limitations, and is a quantitative contribution to an existing discussion on how much disaster-related displacement can be modelled, and in how far assumptions can be generalized. These insights also support a better understanding of displacement and migration from future climate risks.

The devastating 2022 summer flood in Pakistan displaced about 7 million people in the Sindh province alone. Up to one third of the country’s area, mostly the country’s south, was flooded. Effective response to intensifying and compounding climate change hazards requires impact assessments to include socio-economic components, as well as uncertainties arising from the dynamic interactions between impacts. Such quantitative evidence largely remains limited and fragmented, due to methodological challenges and data limitations. Using the open-source impact assessment platform CLIMADA, we study to what extent flood-related hazards can be used to quantify displacement outcomes in a data-limited region. Using flood depths, exposed population, and impact functions, we link flood vulnerability to displaced people. This allows us to estimate internal displacement resulting from the flood event, and to further assess how displacement varies across different areas. We find that a flood depth threshold of 0.67m (CI 0.35 - 1.10) provides a best fit to all data from Sindh province. We find a negative correlation between displacement and the degree of urbanisation. By testing the performance of our model in explaining differing displacement estimates reported across Pakistan, we show the limitations of existing impact assessment frameworks. We emphasise the importance of estimating potential displacement alongside other impacts to better characterise, communicate, and ultimately respond to the impacts of floods.